How do I securely obtain my Qase Token?

Log in to Qase.io and click your profile icon to go to **Account settings**. Select **API Tokens** (or sometimes found under Apps for an integration token), and click **Create a new API token**. Add a name, click generate, and copy the string provided. It takes exactly 15 seconds. Paste it here to authenticate. Your token is encrypted at rest and injected securely at runtime.

Can my AI write test scripts using the case details?

Absolutely. Inside your IDE (like Cursor), you can ask the agent to 'Fetch case #12 from Qase project PROJ'. The tool retrieves the precise steps, preconditions, and expected results. The agent can then automatically generate Playwright, Cypress, or Selenium scripts based exactly on those Qase definitions.

How can I check the results of a recent QA cycle?

Ask your agent to `list_runs` for your project. This will surface your recent executions. If you notice a run with a high failure rate, ask the agent to pull `get_run` with that run's ID to dive into specifics and see which modules failed the automated checks.

Can it help me track Jira bugs linked to tests?

Yes. By using the `list_defects` capability, your AI can pull all registered defects in a Qase project. If your Qase is integrated with Jira or GitHub, the returned defect data includes external issue links, helping developers immediately map a failed test to the corresponding engineering ticket.

Qase MCP Server for CrewAI 10 tools — connect in under 2 minutes

Name: Qase
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 10 Tools Framework

Connect your CrewAI agents to Qase through Vinkius, pass the Edge URL in the `mcps` parameter and every Qase tool is auto-discovered at runtime. No credentials to manage, no infrastructure to maintain.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

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Vinkius supports streamable HTTP and SSE.

python

from crewai import Agent, Task, Crew

agent = Agent(
    role="Qase Specialist",
    goal="Help users interact with Qase effectively",
    backstory=(
        "You are an expert at leveraging Qase tools "
        "for automation and data analysis."
    ),
    # Your Vinkius token. get it at cloud.vinkius.com
    mcps=["https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"],
)

task = Task(
    description=(
        "Explore all available tools in Qase "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 10 available tools "
        "and what they can do."
    ),
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

Fully ManagedVinkius Servers

60%Token savings

High SecurityEnterprise-grade

IAMAccess control

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DLPData protection

V8 IsolateSandboxed

Ed25519Audit chain

<40msKill switch

Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Qase MCP Server

Connect your Qase workspace to any AI agent and integrate test management deeply into your development workflow.

When paired with CrewAI, Qase becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call Qase tools autonomously, one agent queries data, another analyzes results, a third compiles reports, all orchestrated through Vinkius with zero configuration overhead.

What you can do

Project Overviews — Retrieve all active projects, view health metrics, and get total counts of test cases, runs, and defects instantly
Test Cases & Suites — Explore your test hierarchy, pull specific test steps, and check case automation statuses without opening the Qase dashboard
Test Runs & Execution — List all test runs, monitor execution status (passed, failed, blocked), and dive deep into test run analytics
Defects & Milestones — Track project milestones and extract all logged defects linked to failed test cases, complete with severity levels and issue links

The Qase MCP Server exposes 10 tools through the Vinkius. Connect it to CrewAI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Qase to CrewAI via MCP

Follow these steps to integrate the Qase MCP Server with CrewAI.

Install CrewAI

Run pip install crewai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com

Customize the agent

Adjust the role, goal, and backstory to fit your use case

Run the crew

Run python crew.py. CrewAI auto-discovers 10 tools from Qase

Why Use CrewAI with the Qase MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with Qase through the Model Context Protocol.

Multi-agent collaboration lets you decompose complex workflows into specialized roles, one agent researches, another analyzes, a third generates reports, each with access to MCP tools

CrewAI's native MCP integration requires zero adapter code: pass Vinkius Edge URL directly in the `mcps` parameter and agents auto-discover every available tool at runtime

Built-in task delegation and shared memory mean agents can pass context between steps without manual state management, enabling multi-hop reasoning across tool calls

Sequential and hierarchical crew patterns map naturally to real-world workflows: enumerate subdomains → analyze DNS history → check WHOIS records → compile findings into actionable reports

Qase + CrewAI Use Cases

Practical scenarios where CrewAI combined with the Qase MCP Server delivers measurable value.

Automated multi-step research: a reconnaissance agent queries Qase for raw data, then a second analyst agent cross-references findings and flags anomalies. all without human handoff

Scheduled intelligence reports: set up a crew that periodically queries Qase, analyzes trends over time, and generates executive briefings in markdown or PDF format

Multi-source enrichment pipelines: chain Qase tools with other MCP servers in the same crew, letting agents correlate data across multiple providers in a single workflow

Compliance and audit automation: a compliance agent queries Qase against predefined policy rules, generates deviation reports, and routes findings to the appropriate team

Qase MCP Tools for CrewAI (10)

These 10 tools become available when you connect Qase to CrewAI via MCP:

get_case

Retrieves details for a specific test case

get_project

Retrieves details for a specific project

get_run

Retrieves details for a specific test run

list_cases

Lists test cases in a project

list_defects

Lists all defects linked to test case failures

list_milestones

Lists all milestones in a project

list_plans

Lists all test plans in a project

list_projects

Lists all projects in Qase

list_runs

Lists all test runs in a project

list_suites

Lists test suites in a project

Example Prompts for Qase in CrewAI

Ready-to-use prompts you can give your CrewAI agent to start working with Qase immediately.

"List all Qase projects and show me their overall health."

"Fetch the details of test case ID 45 in the WEB project."

"Are there any recent defects added for the WEB project?"

Troubleshooting Qase MCP Server with CrewAI

Common issues when connecting Qase to CrewAI through the Vinkius, and how to resolve them.

MCP tools not discovered

Ensure the Edge URL is correct. CrewAI connects lazily when the crew starts. check console output.

Agent not using tools

Make the task description specific. Instead of "do something", say "Use the available tools to list contacts".

Timeout errors

CrewAI has a 10s connection timeout by default. Ensure your network can reach the Edge URL.

Rate limiting or 429 errors

Vinkius enforces per-token rate limits. Check your subscription tier and request quota in the dashboard. Upgrade if you need higher throughput.

Qase + CrewAI FAQ

Common questions about integrating Qase MCP Server with CrewAI.

How does CrewAI discover and connect to MCP tools?

CrewAI connects to MCP servers lazily. when the crew starts, each agent resolves its MCP URLs and fetches the tool catalog via the standard tools/list method. This means tools are always fresh and reflect the server's current capabilities. No tool schemas need to be hardcoded.

Can different agents in the same crew use different MCP servers?

Yes. Each agent has its own mcps list, so you can assign specific servers to specific roles. For example, a reconnaissance agent might use a domain intelligence server while an analysis agent uses a vulnerability database server.

What happens when an MCP tool call fails during a crew run?

CrewAI wraps tool failures as context for the agent. The LLM receives the error message and can decide to retry with different parameters, fall back to a different tool, or mark the task as partially complete. This resilience is critical for production workflows.

Can CrewAI agents call multiple MCP tools in parallel?

CrewAI agents execute tool calls sequentially within a single reasoning step. However, you can run multiple agents in parallel using process=Process.parallel, each calling different MCP tools concurrently. This is ideal for workflows where separate data sources need to be queried simultaneously.

Can I run CrewAI crews on a schedule (cron)?

Yes. CrewAI crews are standard Python scripts, so you can invoke them via cron, Airflow, Celery, or any task scheduler. The crew.kickoff() method runs synchronously by default, making it straightforward to integrate into existing pipelines.